A Literature Survey: Neural Networks for object detection

EOI: 10.11242/viva-tech.01.01.09

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Aishwarya Sarkale, Kaiwant Shah, Anandji Chaudhary, Tatwadarshi P. N., "A Literature Survey: Neural Networks for object detection", VIVA-Tech IJRI Volume 1, Issue 1, Article 9, pp. 1-9, Oct 2018. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.


Humans have a great capability to distinguish objects by their vision. But, for machines object detection is an issue. Thus, Neural Networks have been introduced in the field of computer science. Neural Networks are also called as ‘Artificial Neural Networks’ [13]. Artificial Neural Networks are computational models of the brain which helps in object detection and recognition. This paper describes and demonstrates the different types of Neural Networks such as ANN, KNN, FASTER R-CNN, 3D-CNN, RNN etc. with their accuracies. From the study of various research papers, the accuracies of different Neural Networks are discussed and compared and it can be concluded that in the given test cases, the ANN gives the best accuracy for the object detection.


ANN, Neural Networks, Object Detection.


  1. F. Pourghahestani, E. Rashedi, “Object detection in images using artificial neural network and improved binary gravitational search algorithm”, 2015 4th IEEE CFIS.
  2. C. Lee, K. Won oh, H. Kim, “Comparison of faster R-CNN models for object detection”, 2016 16th International Conference on Control, Automation and Systems, 16–19, 2016 in HICO.
  3. A. Nguyen, D. Kanoulas, G. Caldwell, and N. Tsagarakis, “Detecting Object Affordances with Convolutional Neural Networks”, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 9-14, 2016.
  4. Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, J. Xiao, “3D Shapenets: A deep representation for volumetric shapes”, 2015 IEEE, 978-1-4673-6964-0/15.
  5. N. Gunji, H. Niigaki, K. Tsutsuguchi, T. Kurozumi, and T. Kinebuchi, “3D Object recognition from large-scale point clouds with global descriptor and sliding window” , 2016 IEEE 23rd International Conference on Pattern Recognition (ICPR), December 4-8, 2016.
  6. X. Zhou, W. Gong, W. Fu, F. Du, “Application of deep learning in object detection”, 2017 IEEE ICIS, May 24-26,2017.
  7. J. Cruz, M. Dimaala, L. Francisco, E. Franco, A. Bandala, E. Dadios, “Object recognition and detection by shape and color pattern recognition using ANN”, 2013 IEEE 2013 International Conference of Information and Communication Technology, 2013 IEEE.
  8. A. Caglayan, A. Can, “3D Convolutional Object Recognition using Volumetric Representations of Depth Data”, 2017 Fifteenth IAPR International Conference on Machine Vision Applications, MVA.
  9. S. Yadav, A. Singh, “An Image Matching and Object Recognition System using Webcam Robot”, 2016 PDGC, IEEE.
  10. D. Erhan, C. Szegedy, A. Toshev, D. Anguelov, “Scalable Object Detection using Deep Neural Networks”, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
  11. Y. Wang, Q. Qiu, “FPGA Acceleration of Recurrent Neural Network based Language Model”, 2015 IEEE 23rd Annual International Symposium on Field-Programmable Custom Computing Machines.
  12. J. Kim, V. Pavlovic, “A Shape Preserving Approach for Salient Object Detection Using Convolutional Neural Networks”, 2016 23rd International Conference on Pattern Recognition (ICPR),IEEE.
  13. S.N. Sivanandam, S.N. Deepa, Introduction to neural networks using MATLAB 6.0 (Tata McGraw Hill Education, 2006).
  14. Ramesh Jain, Rangachar Kasturi, Brain G. Schunck, Machine vision, (Tata McGraw Hill Education, 1995).